咨询与建议

限定检索结果

文献类型

  • 42 篇 期刊文献
  • 13 篇 会议

馆藏范围

  • 55 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 47 篇 工学
    • 29 篇 计算机科学与技术...
    • 28 篇 软件工程
    • 17 篇 信息与通信工程
    • 14 篇 光学工程
    • 12 篇 电气工程
    • 11 篇 生物工程
    • 10 篇 生物医学工程(可授...
    • 9 篇 电子科学与技术(可...
    • 2 篇 机械工程
    • 2 篇 控制科学与工程
    • 2 篇 建筑学
    • 2 篇 土木工程
    • 2 篇 测绘科学与技术
    • 2 篇 化学工程与技术
    • 1 篇 材料科学与工程(可...
  • 28 篇 理学
    • 14 篇 物理学
    • 11 篇 生物学
    • 8 篇 数学
    • 5 篇 统计学(可授理学、...
    • 2 篇 化学
  • 13 篇 管理学
    • 10 篇 图书情报与档案管...
    • 4 篇 管理科学与工程(可...
  • 4 篇 医学
    • 4 篇 临床医学
    • 3 篇 基础医学(可授医学...
    • 2 篇 药学(可授医学、理...
    • 1 篇 公共卫生与预防医...
  • 1 篇 法学
    • 1 篇 社会学

主题

  • 6 篇 magnetic resonan...
  • 4 篇 contrastive lear...
  • 3 篇 convolutional ne...
  • 3 篇 unsupervised lea...
  • 2 篇 deep neural netw...
  • 2 篇 statistics
  • 2 篇 recovery
  • 2 篇 labeled data
  • 2 篇 semantics
  • 2 篇 artificial intel...
  • 2 篇 vehicles
  • 2 篇 image reconstruc...
  • 2 篇 medical image pr...
  • 1 篇 image enhancemen...
  • 1 篇 covid-19
  • 1 篇 surveys
  • 1 篇 semantic segment...
  • 1 篇 deep learning
  • 1 篇 ophthalmology
  • 1 篇 image segmentati...

机构

  • 28 篇 shenzhen key lab...
  • 15 篇 shenzhen key lab...
  • 10 篇 peng cheng labor...
  • 9 篇 school of comput...
  • 8 篇 school of comput...
  • 8 篇 bio-computing re...
  • 6 篇 pengcheng labora...
  • 6 篇 the shenzhen key...
  • 4 篇 inception instit...
  • 4 篇 department of el...
  • 4 篇 school of cyber ...
  • 4 篇 shenzhen institu...
  • 4 篇 harbin institute...
  • 3 篇 terminus group
  • 3 篇 harbin institute...
  • 3 篇 the bio-computin...
  • 3 篇 college of compu...
  • 3 篇 department of co...
  • 2 篇 college of big d...
  • 2 篇 school of fashio...

作者

  • 35 篇 xu yong
  • 23 篇 wen jie
  • 9 篇 liu chengliang
  • 8 篇 feng chun-mei
  • 7 篇 fei lunke
  • 7 篇 zhang zheng
  • 7 篇 fu huazhu
  • 6 篇 shao ling
  • 5 篇 jie wen
  • 5 篇 yong xu
  • 5 篇 tian chunwei
  • 5 篇 wu zhihao
  • 5 篇 luo xiaoling
  • 5 篇 lin chia-wen
  • 5 篇 zuo wangmeng
  • 4 篇 yan yunlu
  • 4 篇 huang chao
  • 4 篇 zhang bob
  • 4 篇 zhang david
  • 4 篇 sun lilei

语言

  • 40 篇 英文
  • 15 篇 其他
检索条件"机构=Shenzhen Key Laboratory of Visual Object Detection and Recognition"
55 条 记 录,以下是41-50 订阅
Global-Supervised Contrastive Loss and View-Aware-Based Post-Processing for Vehicle Re-Identification
arXiv
收藏 引用
arXiv 2022年
作者: Hu, Zhijun Xu, Yong Wen, Jie Cheng, Xianjing Zhang, Zaijun Sun, Lilei Wang, Yaowei School of Mathematics and Statistics Guangxi Normal University Guilin541004 China College of Computer Science and Technology Guizhou University Guiyang550025 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen China College of Computer Science and Technology Guizhou University Guiyang550025 China Key Laboratory of Complex Systems and Intelligent Computing School of Mathematics and Statistics Qiannan Normal University for Nationalities Duyun558000 China Peng Cheng Laboratory Shenzhen518055 China
In this paper, we propose a Global-Supervised Contrastive loss (LGSupCon) and a view-aware-based post-processing (VABPP) method for the field of vehicle re-identification. The traditional supervised contrastive loss (... 详细信息
来源: 评论
Asymmetric CNN for image super-resolution
arXiv
收藏 引用
arXiv 2021年
作者: Tian, Chunwei Xu, Yong Zuo, Wangmeng Lin, Chia-Wen Zhang, David The Bio-Computing Research Center Harbin Institute of Technology ShenzhenShenzhenGuangdong518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition ShenzhenGuangdong518055 China The School of Computer Science and Technology Harbin Institute of Technology HarbinHeilongjiang150001 China The Peng Cheng Laboratory ShenzhenGuangdong518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen Guangdong518055 China The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan ShenzhenGuangdong518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China
Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. According to nature of different applications, designing appropriate CNN architectures is developed. Ho... 详细信息
来源: 评论
Dual-Octave Convolution for accelerated parallel MR image reconstruction
arXiv
收藏 引用
arXiv 2021年
作者: Feng, Chun-Mei Yang, Zhanyuan Chen, Geng Xu, Yong Shao, Ling Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China School of Automation Engineering University of Electronic Science and Technology of China China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates
Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration by obtaining multiple undersampled images simultaneously through parallel imaging has always been the subject of researc... 详细信息
来源: 评论
Deep Multi-View Contrastive Clustering via Graph Structure Awareness
收藏 引用
IEEE Transactions on Image Processing 2025年
作者: Fei, Lunke He, Junlin Zhu, Qi Zhao, Shuping Wen, Jie Xu, Yong Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China
Multi-view clustering (MVC) aims to exploit the latent relationships between heterogeneous samples in an unsupervised manner, which has served as a fundamental task in the unsupervised learning community and has drawn... 详细信息
来源: 评论
Specificity-Preserving Federated Learning for MR Image Reconstruction
arXiv
收藏 引用
arXiv 2021年
作者: Feng, Chun-Mei Yan, Yunlu Wang, Shanshan Xu, Yong Shao, Ling Fu, Huazhu The Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China The Institute of High Performance Computing A*STAR Singapore138632 Singapore Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology CAS Shenzhen518055 China Terminus Group China
Federated learning (FL) can be used to improve data privacy and efficiency in magnetic resonance (MR) image reconstruction by enabling multiple institutions to collaborate without needing to aggregate local data. Howe... 详细信息
来源: 评论
CDIMC-net: Cognitive deep incomplete multi-view clustering network  29
CDIMC-net: Cognitive deep incomplete multi-view clustering n...
收藏 引用
29th International Joint Conference on Artificial Intelligence, IJCAI 2020
作者: Wen, Jie Zhang, Zheng Xu, Yong Zhang, Bob Fei, Lunke Xie, Guo-Sen Bio-Computing Research Center Harbin Institute of Technology Shenzhen Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen China Pengcheng Laboratory Shenzhen China Department of Computer and Information Science University of Macau Taipa China School of Computer Science and Technology Guangdong University of Technology Guangzhou China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates
In recent years, incomplete multi-view clustering, which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Although a series of methods have been proposed... 详细信息
来源: 评论
Multi-modal Aggregation Network for fast MR imaging
arXiv
收藏 引用
arXiv 2021年
作者: Feng, Chun-Mei Fu, Huazhu Zhou, Tianfei Xu, Yong Shao, Ling Zhang, David The Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China Singapore138632 Singapore The Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates ETH Zurich Switzerland Switzerland Shenzhen518172 China The Shenzhen Research Institute of Big Data Shenzhen518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China
Magnetic resonance (MR) imaging is a commonly used scanning technique for disease detection, diagnosis and treatment monitoring. Although it is able to produce detailed images of organs and tissues with better contras... 详细信息
来源: 评论
Multi-Modal Transformer for Accelerated MR Imaging
arXiv
收藏 引用
arXiv 2021年
作者: Feng, Chun-Mei Yan, Yunlu Chen, Geng Xu, Yong Hu, Ying Shao, Ling Fu, Huazhu Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Terminus Group China Institute of High Performance Computing A*STAR Singapore138632 Singapore
Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guida... 详细信息
来源: 评论
Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution
arXiv
收藏 引用
arXiv 2021年
作者: Feng, Chun-Mei Yan, Yunlu Yu, Kai Xu, Yong Yang, Jian Shao, Ling Fu, Huazhu The Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China The Institute of High Performance Computing A*STAR Singapore138632 Singapore The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China The Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Terminus Group China
Super-resolving the Magnetic Resonance (MR) image of a target contrast under the guidance of the corresponding auxiliary contrast, which provides additional anatomical information, is a new and effective solution for ... 详细信息
来源: 评论
Vehicle re-identification based on dual distance center loss
arXiv
收藏 引用
arXiv 2020年
作者: Hu, Zhijun Xu, Yong Wen, Jie Sun, Lilei Raja, S.P. College of Computer Science and Technology Guizhou University Guiyang550025 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen China Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D ChennaiTamilnadu India
Recently, deep learning has been widely used in the field of vehicle re-identification. When training a deep model, softmax loss is usually used as a supervision tool. However, the softmax loss performs well for close... 详细信息
来源: 评论